import os
import time
import random
import itertools
from functools import partial
from contextlib import contextmanager

import numpy as np
from tqdm import tqdm
from einops import rearrange, repeat

import torch
import torch.nn as nn
import pytorch_lightning as pl
from torchvision.utils import make_grid
from torch.optim.lr_scheduler import LambdaLR
from pytorch_lightning.utilities import rank_zero_only
from videocrafter.lvdm.models.modules.distributions import normal_kl, DiagonalGaussianDistribution
from videocrafter.lvdm.models.modules.util import make_beta_schedule, extract_into_tensor, noise_like
from videocrafter.lvdm.models.modules.lora import inject_trainable_lora
from videocrafter.lvdm.samplers.ddim import DDIMSampler
from videocrafter.lvdm.utils.common_utils import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config, check_istarget


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


def uniform_on_device(r1, r2, shape, device):
    return (r1 - r2) * torch.rand(*shape, device=device) + r2


def split_video_to_clips(video, clip_length, drop_left=True):
    video_length = video.shape[2]
    shape = video.shape
    if video_length % clip_length != 0 and drop_left:
        video = video[:, :, :video_length // clip_length * clip_length, :, :]
        print(f'[split_video_to_clips] Drop frames from {shape} to {video.shape}')
    nclips = video_length // clip_length
    clips = rearrange(video, 'b c (nc cl) h w -> (b nc) c cl h w', cl=clip_length, nc=nclips)
    return clips

def merge_clips_to_videos(clips, bs):
    nclips = clips.shape[0] // bs
    video = rearrange(clips, '(b nc) c t h w -> b c (nc t) h w', nc=nclips)
    return video

class DDPM(pl.LightningModule):
    # classic DDPM with Gaussian diffusion, in pixel space
    def __init__(self,
                 unet_config,
                 timesteps=1000,
                 beta_schedule="linear",
                 loss_type="l2",
                 ckpt_path=None,
                 ignore_keys=[],
                 load_only_unet=False,
                 monitor="val/loss",
                 use_ema=True,
                 first_stage_key="image",
                 image_size=256,
                 video_length=None,
                 channels=3,
                 log_every_t=100,
                 clip_denoised=True,
                 linear_start=1e-4,
                 linear_end=2e-2,
                 cosine_s=8e-3,
                 given_betas=None,
                 original_elbo_weight=0.,
                 v_posterior=0.,
                 l_simple_weight=1.,
                 conditioning_key=None,
                 parameterization="eps",
                 scheduler_config=None,
                 learn_logvar=False,
                 logvar_init=0.,
                 *args, **kwargs
                 ):
        super().__init__()
        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
        self.parameterization = parameterization
        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
        self.cond_stage_model = None
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.image_size = image_size  # try conv?
        
        if isinstance(self.image_size, int):
            self.image_size = [self.image_size, self.image_size]
        self.channels = channels
        self.model = DiffusionWrapper(unet_config, conditioning_key)
        self.conditioning_key = conditioning_key # also register conditioning_key in diffusion
        
        self.temporal_length = video_length if video_length is not None else unet_config.params.temporal_length
        count_params(self.model, verbose=True)
        self.use_ema = use_ema
        
        self.use_scheduler = scheduler_config is not None
        if self.use_scheduler:
            self.scheduler_config = scheduler_config

        self.v_posterior = v_posterior
        self.original_elbo_weight = original_elbo_weight
        self.l_simple_weight = l_simple_weight

        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)

        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)

        self.loss_type = loss_type

        self.learn_logvar = learn_logvar
        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
        if self.learn_logvar:
            self.logvar = nn.Parameter(self.logvar, requires_grad=True)

    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        if exists(given_betas):
            betas = given_betas
        else:
            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
                                       cosine_s=cosine_s)
        alphas = 1. - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer('betas', to_torch(betas))
        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
                    1. - alphas_cumprod) + self.v_posterior * betas
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.register_buffer('posterior_variance', to_torch(posterior_variance))
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
        self.register_buffer('posterior_mean_coef1', to_torch(
            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
        self.register_buffer('posterior_mean_coef2', to_torch(
            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))

        if self.parameterization == "eps":
            lvlb_weights = self.betas ** 2 / (
                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
        elif self.parameterization == "x0":
            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
        else:
            raise NotImplementedError("mu not supported")
        # TODO how to choose this term
        lvlb_weights[0] = lvlb_weights[1]
        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
        assert not torch.isnan(self.lvlb_weights).all()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.model.parameters())
            self.model_ema.copy_to(self.model)
            if context is not None:
                print(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.model.parameters())
                if context is not None:
                    print(f"{context}: Restored training weights")

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik) or (ik.startswith('**') and ik.split('**')[-1] in k):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
            sd, strict=False)
        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def q_mean_variance(self, x_start, t):
        """
        Get the distribution q(x_t | x_0).
        :param x_start: the [N x C x ...] tensor of noiseless inputs.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
        """
        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
        return mean, variance, log_variance

    def predict_start_from_noise(self, x_t, t, noise):
        return (
                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(self, x, t, clip_denoised: bool):
        model_out = self.model(x, t)
        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        if clip_denoised:
            x_recon.clamp_(-1., 1.)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
        return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
        b, *_, device = *x.shape, x.device
        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
        noise = noise_like(x.shape, device, repeat_noise)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def p_sample_loop(self, shape, return_intermediates=False):
        device = self.betas.device
        b = shape[0]
        img = torch.randn(shape, device=device)
        intermediates = [img]
        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
                                clip_denoised=self.clip_denoised)
            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
                intermediates.append(img)
        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(self, batch_size=16, return_intermediates=False):
        channels = self.channels
        video_length = self.total_length
        size = (batch_size, channels, video_length, *self.image_size)
        return self.p_sample_loop(size,
                                  return_intermediates=return_intermediates)

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)

    def get_loss(self, pred, target, mean=True, mask=None):
        if self.loss_type == 'l1':
            loss = (target - pred).abs()
            if mean:
                loss = loss.mean()
        elif self.loss_type == 'l2':
            if mean:
                loss = torch.nn.functional.mse_loss(target, pred)
            else:
                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
        else:
            raise NotImplementedError("unknown loss type '{loss_type}'")
        if mask is not None:
            assert(mean is False)
            assert(loss.shape[2:] == mask.shape[2:]) #thw need be the same
            loss = loss * mask
        return loss

    def p_losses(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_out = self.model(x_noisy, t)

        loss_dict = {}
        if self.parameterization == "eps":
            target = noise
        elif self.parameterization == "x0":
            target = x_start
        else:
            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")

        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3, 4])

        log_prefix = 'train' if self.training else 'val'

        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
        loss_simple = loss.mean() * self.l_simple_weight

        loss_vlb = (self.lvlb_weights[t] * loss).mean()
        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})

        loss = loss_simple + self.original_elbo_weight * loss_vlb

        loss_dict.update({f'{log_prefix}/loss': loss})

        return loss, loss_dict

    def forward(self, x, *args, **kwargs):
        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
        return self.p_losses(x, t, *args, **kwargs)

    def get_input(self, batch, k):
        x = batch[k]
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    def shared_step(self, batch):
        x = self.get_input(batch, self.first_stage_key)
        loss, loss_dict = self(x)
        return loss, loss_dict

    def training_step(self, batch, batch_idx):
        loss, loss_dict = self.shared_step(batch)

        self.log_dict(loss_dict, prog_bar=True,
                      logger=True, on_step=True, on_epoch=True)

        self.log("global_step", self.global_step,
                 prog_bar=True, logger=True, on_step=True, on_epoch=False)

        if self.use_scheduler:
            lr = self.optimizers().param_groups[0]['lr']
            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
        
        if self.log_time:
            total_train_time = (time.time() - self.start_time) / (3600*24)
            avg_step_time = (time.time() - self.start_time) / (self.global_step + 1)
            left_time_2w_step = (20000-self.global_step -1) * avg_step_time / (3600*24)
            left_time_5w_step = (50000-self.global_step -1) * avg_step_time / (3600*24)
            with open(self.logger_path, 'w') as f:
                print(f'total_train_time = {total_train_time:.1f} days \n\
                      total_train_step = {self.global_step + 1} steps \n\
                      left_time_2w_step = {left_time_2w_step:.1f} days \n\
                      left_time_5w_step = {left_time_5w_step:.1f} days', file=f)
        return loss

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        # _, loss_dict_no_ema = self.shared_step_validate(batch)
        # with self.ema_scope():
        #     _, loss_dict_ema = self.shared_step_validate(batch)
        #     loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
        # self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
        # self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
        if (self.global_step) % self.val_fvd_interval == 0 and self.global_step != 0:
            print(f'sample for fvd...')
            self.log_images_kwargs = {
                'inpaint': False,
                'plot_diffusion_rows': False,
                'plot_progressive_rows': False,
                'ddim_steps': 50,
                'unconditional_guidance_scale': 15.0,
            }
            torch.cuda.empty_cache()
            logs = self.log_images(batch, **self.log_images_kwargs)
            self.log("batch_idx", batch_idx,
                    prog_bar=True, on_step=True, on_epoch=False)
            return {'real': logs['inputs'], 'fake': logs['samples'], 'conditioning_txt_img': logs['conditioning_txt_img']}
    
    def get_condition_validate(self, prompt):
        """ text embd
        """
        if isinstance(prompt, str):
            prompt = [prompt]
        c = self.get_learned_conditioning(prompt)
        bs = c.shape[0]
        
        return c
    
    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self.model)
        
    def training_epoch_end(self, outputs):
        
        if (self.current_epoch == 0) or self.resume_new_epoch == 0:
            self.epoch_start_time = time.time()
            self.current_epoch_time = 0
            self.total_time = 0
            self.epoch_time_avg = 0
        else:
            self.current_epoch_time = time.time() - self.epoch_start_time
            self.epoch_start_time = time.time()
            self.total_time += self.current_epoch_time
            self.epoch_time_avg = self.total_time / self.current_epoch
        self.resume_new_epoch += 1
        epoch_avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
        
        self.log('train/epoch/loss', epoch_avg_loss, logger=True, on_epoch=True)
        self.log('train/epoch/idx', self.current_epoch, logger=True, on_epoch=True)
        self.log('train/epoch/time', self.current_epoch_time, logger=True, on_epoch=True)
        self.log('train/epoch/time_avg', self.epoch_time_avg, logger=True, on_epoch=True)
        self.log('train/epoch/time_avg_min', self.epoch_time_avg / 60, logger=True, on_epoch=True)

    def _get_rows_from_list(self, samples):
        n_imgs_per_row = len(samples)
        denoise_grid = rearrange(samples, 'n b c t h w -> b n c t h w')
        denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
        denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    @torch.no_grad()
    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, 
                   plot_diffusion_rows=True, plot_denoise_rows=True, **kwargs):
        """ log images for DDPM """
        log = dict()
        x = self.get_input(batch, self.first_stage_key)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        x = x.to(self.device)[:N]
        log["inputs"] = x
        if 'fps' in batch:
            log['fps'] = batch['fps']

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            x_start = x[:n_row]

            for t in range(self.num_timesteps):
                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
                    t = t.to(self.device).long()
                    noise = torch.randn_like(x_start)
                    x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
                    diffusion_row.append(x_noisy)

            log["diffusion_row"] = self._get_rows_from_list(diffusion_row)

        if sample:
            # get denoise row
            with self.ema_scope("Plotting"):
                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)

            log["samples"] = samples
            if plot_denoise_rows:
                log["denoise_row"] = self._get_rows_from_list(denoise_row)

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.learn_logvar:
            params = params + [self.logvar]
        opt = torch.optim.AdamW(params, lr=lr)
        return opt


class LatentDiffusion(DDPM):
    """main class"""
    def __init__(self,
                 first_stage_config,
                 cond_stage_config,
                 num_timesteps_cond=None,
                 cond_stage_key="image",
                 cond_stage_trainable=False,
                 concat_mode=True,
                 cond_stage_forward=None,
                 conditioning_key=None,
                 scale_factor=1.0,
                 scale_by_std=False,
                 encoder_type="2d",
                 shift_factor=0.0,
                 split_clips=True,
                 downfactor_t=None,
                 clip_length=None,
                 only_model=False,
                 lora_args={},
                 *args, **kwargs):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs['timesteps']
        # for backwards compatibility after implementation of DiffusionWrapper
        
        if conditioning_key is None:
            conditioning_key = 'concat' if concat_mode else 'crossattn'
        if cond_stage_config == '__is_unconditional__':
            conditioning_key = None
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer('scale_factor', torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.instantiate_cond_stage(cond_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None  
        self.cond_stage_config = cond_stage_config
        self.first_stage_config = first_stage_config
        self.encoder_type = encoder_type
        assert(encoder_type in ["2d", "3d"])
        self.restarted_from_ckpt = False
        self.shift_factor = shift_factor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
            self.restarted_from_ckpt = True
        self.split_clips = split_clips
        self.downfactor_t = downfactor_t
        self.clip_length = clip_length
        # lora related args
        self.inject_unet = getattr(lora_args, "inject_unet", False)
        self.inject_clip = getattr(lora_args, "inject_clip", False)
        self.inject_unet_key_word = getattr(lora_args, "inject_unet_key_word", None)
        self.inject_clip_key_word = getattr(lora_args, "inject_clip_key_word", None)
        self.lora_rank = getattr(lora_args, "lora_rank", 4)

    def make_cond_schedule(self, ):
        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
        self.cond_ids[:self.num_timesteps_cond] = ids

    def inject_lora(self, lora_scale=1.0):
        if self.inject_unet:
            self.lora_require_grad_params, self.lora_names = inject_trainable_lora(self.model, self.inject_unet_key_word, 
                                                                                   r=self.lora_rank,
                                                                                   scale=lora_scale
                                                                                   )
        if self.inject_clip:
            self.lora_require_grad_params_clip, self.lora_names_clip = inject_trainable_lora(self.cond_stage_model, self.inject_clip_key_word, 
                                                                                             r=self.lora_rank,
                                                                                             scale=lora_scale
                                                                                             )

    @rank_zero_only
    @torch.no_grad()
    def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None):
        # only for very first batch, reset the self.scale_factor
        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
            # set rescale weight to 1./std of encodings
            print("### USING STD-RESCALING ###")
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.device)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
            del self.scale_factor
            self.register_buffer('scale_factor', 1. / z.flatten().std())
            print(f"setting self.scale_factor to {self.scale_factor}")
            print("### USING STD-RESCALING ###")
            print(f"std={z.flatten().std()}")

    def register_schedule(self,
                          given_betas=None, beta_schedule="linear", timesteps=1000,
                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)

        self.shorten_cond_schedule = self.num_timesteps_cond > 1
        if self.shorten_cond_schedule:
            self.make_cond_schedule()

    def instantiate_first_stage(self, config):
        model = instantiate_from_config(config)
        self.first_stage_model = model.eval()
        self.first_stage_model.train = disabled_train
        for param in self.first_stage_model.parameters():
            param.requires_grad = False

    def instantiate_cond_stage(self, config):
        if config is None:
            self.cond_stage_model = None
            return
        if not self.cond_stage_trainable:
            if config == "__is_first_stage__":
                print("Using first stage also as cond stage.")
                self.cond_stage_model = self.first_stage_model
            elif config == "__is_unconditional__":
                print(f"Training {self.__class__.__name__} as an unconditional model.")
                self.cond_stage_model = None
            else:
                model = instantiate_from_config(config)
                self.cond_stage_model = model.eval()
                self.cond_stage_model.train = disabled_train
                for param in self.cond_stage_model.parameters():
                    param.requires_grad = False
        else:
            assert config != '__is_first_stage__'
            assert config != '__is_unconditional__'
            model = instantiate_from_config(config)
            self.cond_stage_model = model


    def get_first_stage_encoding(self, encoder_posterior, noise=None):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample(noise=noise)
        elif isinstance(encoder_posterior, torch.Tensor):
            z = encoder_posterior
        else:
            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
        z = self.scale_factor * (z + self.shift_factor)
        return z


    def get_learned_conditioning(self, c):
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
                c = self.cond_stage_model.encode(c)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
        return c


    @torch.no_grad()
    def get_condition(self, batch, x, bs, force_c_encode, k, cond_key, is_imgs=False):
        is_conditional = self.model.conditioning_key is not None # crossattn
        if is_conditional:
            if cond_key is None:
                cond_key = self.cond_stage_key 
            
            # get condition batch of different condition type
            if cond_key != self.first_stage_key:
                assert(cond_key in ["caption", "txt"])
                xc = batch[cond_key]
            else:
                xc = x
            
            # if static video
            if self.static_video:
                xc_ = [c + ' (static)' for c in xc]
                xc = xc_
            
            # get learned condition.
            # can directly skip it: c = xc
            if self.cond_stage_config is not None and (not self.cond_stage_trainable or force_c_encode):
                if isinstance(xc, torch.Tensor):
                    xc = xc.to(self.device)
                c = self.get_learned_conditioning(xc)
            else:
                c = xc

            if self.classfier_free_guidance:
                if cond_key in ['caption', "txt"] and self.uncond_type == 'empty_seq':
                    for i, ci in enumerate(c):
                        if random.random() < self.prob:
                            c[i] = ""
                elif cond_key == 'class_label' and self.uncond_type == 'zero_embed':
                    pass
                elif cond_key == 'class_label' and self.uncond_type == 'learned_embed':
                    import pdb;pdb.set_trace()
                    for i, ci in enumerate(c):
                        if random.random() < self.prob:
                            c[i]['class_label'] = self.n_classes
                    
                else:
                    raise NotImplementedError
                
            if self.zero_cond_embed:
                import pdb;pdb.set_trace()
                c = torch.zeros_like(c)

            # process c
            if bs is not None:
                if (is_imgs and not self.static_video):
                    c = c[:bs*self.temporal_length] # each random img (in T axis) has a corresponding prompt
                else:
                    c = c[:bs]

        else:
            c = None
            xc = None
            
        return c, xc

    @torch.no_grad()
    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
                  cond_key=None, return_original_cond=False, bs=None, mask_temporal=False):
        """ Get input in LDM 
        """
        # get input imgaes
        x = super().get_input(batch, k) # k = first_stage_key=image
        is_imgs = True if k == 'jpg' else False
        if is_imgs:
            if self.static_video:
                # repeat single img to a static video
                x = x.unsqueeze(2) # bchw -> bc1hw
                x = x.repeat(1,1,self.temporal_length,1,1) # bc1hw -> bcthw
            else:
                # rearrange to videos with T random img
                bs_load = x.shape[0] // self.temporal_length
                x = x[:bs_load*self.temporal_length, ...]
                x = rearrange(x, '(b t) c h w -> b c t h w', t=self.temporal_length, b=bs_load)

        if bs is not None:
            x = x[:bs]
        
        x = x.to(self.device)
        x_ori = x
        
        b, _, t, h, w = x.shape
        
        # encode video frames x to z via a 2D encoder
        x = rearrange(x, 'b c t h w -> (b t) c h w')
        encoder_posterior = self.encode_first_stage(x, mask_temporal)
        z = self.get_first_stage_encoding(encoder_posterior).detach()
        z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
        
        
        c, xc = self.get_condition(batch, x, bs, force_c_encode, k, cond_key, is_imgs)
        out = [z, c]
        
        if return_first_stage_outputs:
            xrec = self.decode_first_stage(z, mask_temporal=mask_temporal)
            out.extend([x_ori, xrec])
        if return_original_cond:
            if isinstance(xc, torch.Tensor) and xc.dim() == 4:
                xc = rearrange(xc, '(b t) c h w -> b c t h w', b=b, t=t)
            out.append(xc)
        
        return out
    
    @torch.no_grad()
    def decode(self, z, **kwargs,):
        z = 1. / self.scale_factor * z - self.shift_factor
        results = self.first_stage_model.decode(z,**kwargs)
        return results
    
    @torch.no_grad()
    def decode_first_stage_2DAE(self, z, decode_bs=16, return_cpu=True, **kwargs):
        b, _, t, _, _ = z.shape
        z = rearrange(z, 'b c t h w -> (b t) c h w')
        if decode_bs is None:
            results = self.decode(z, **kwargs)
        else:
            z = torch.split(z, decode_bs, dim=0)
            if return_cpu:
                results = torch.cat([self.decode(z_, **kwargs).cpu() for z_ in z], dim=0)
            else:
                results = torch.cat([self.decode(z_, **kwargs) for z_ in z], dim=0)
        results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t).contiguous()
        return results

    @torch.no_grad()
    def decode_first_stage(self, z, decode_bs=16, return_cpu=True, **kwargs):
        assert(self.encoder_type == "2d" and z.dim() == 5)
        return self.decode_first_stage_2DAE(z, decode_bs=decode_bs, return_cpu=return_cpu, **kwargs)

    @torch.no_grad()
    def encode_first_stage_2DAE(self, x, encode_bs=16):
        b, _, t, _, _ = x.shape
        x = rearrange(x, 'b c t h w -> (b t) c h w')
        if encode_bs is None:
            results = self.first_stage_model.encode(x)
        else:
            x = torch.split(x, encode_bs, dim=0)
            zs = []
            for x_ in x:
                encoder_posterior = self.first_stage_model.encode(x_)
                z = self.get_first_stage_encoding(encoder_posterior).detach()
                zs.append(z)
            results = torch.cat(zs, dim=0)
        results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
        return results
    
    @torch.no_grad()
    def encode_first_stage(self, x):
        assert(self.encoder_type == "2d" and x.dim() == 5)
        b, _, t, _, _ = x.shape
        x = rearrange(x, 'b c t h w -> (b t) c h w')
        results = self.first_stage_model.encode(x)
        results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
        return results

    def shared_step(self, batch, **kwargs):
        """ shared step of LDM.
        If learned condition, c is raw condition (e.g. text)
        Encoding condition is performed in below forward function.
        """
        x, c = self.get_input(batch, self.first_stage_key)
        loss = self(x, c)
        return loss
    
    def forward(self, x, c, *args, **kwargs):
        start_t = getattr(self, "start_t", 0)
        end_t = getattr(self, "end_t", self.num_timesteps)
        t = torch.randint(start_t, end_t, (x.shape[0],), device=self.device).long()
        
        if self.model.conditioning_key is not None:
            assert c is not None
            if self.cond_stage_trainable:
                c = self.get_learned_conditioning(c)
            if self.classfier_free_guidance and self.uncond_type == 'zero_embed':
                for i, ci in enumerate(c):
                    if random.random() < self.prob:
                        c[i] = torch.zeros_like(c[i])
            if self.shorten_cond_schedule:  # TODO: drop this option
                tc = self.cond_ids[t].to(self.device)
                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
        
        return self.p_losses(x, c, t, *args, **kwargs)

    def apply_model(self, x_noisy, t, cond, return_ids=False, **kwargs):

        if isinstance(cond, dict):
            # hybrid case, cond is exptected to be a dict
            pass
        else:
            if not isinstance(cond, list):
                cond = [cond]
            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
            cond = {key: cond}

        x_recon = self.model(x_noisy, t, **cond, **kwargs)

        if isinstance(x_recon, tuple) and not return_ids:
            return x_recon[0]
        else:
            return x_recon

    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)

    def _prior_bpd(self, x_start):
        """
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        """
        batch_size = x_start.shape[0]
        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
        return mean_flat(kl_prior) / np.log(2.0)

    def p_losses(self, x_start, cond, t, noise=None, skip_qsample=False, x_noisy=None, cond_mask=None, **kwargs,):
        if not skip_qsample:
            noise = default(noise, lambda: torch.randn_like(x_start))
            x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        else:
            assert(x_noisy is not None)
            assert(noise is not None)
        model_output = self.apply_model(x_noisy, t, cond, **kwargs)

        loss_dict = {}
        prefix = 'train' if self.training else 'val'

        if self.parameterization == "x0":
            target = x_start
        elif self.parameterization == "eps":
            target = noise
        else:
            raise NotImplementedError()
        
        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3, 4])
        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
        if self.logvar.device != self.device:
            self.logvar = self.logvar.to(self.device)
        logvar_t = self.logvar[t]
        loss = loss_simple / torch.exp(logvar_t) + logvar_t
        if self.learn_logvar:
            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
            loss_dict.update({'logvar': self.logvar.data.mean()})

        loss = self.l_simple_weight * loss.mean()

        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3, 4))
        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
        loss += (self.original_elbo_weight * loss_vlb)
        loss_dict.update({f'{prefix}/loss': loss})

        return loss, loss_dict

    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
                        return_x0=False, score_corrector=None, corrector_kwargs=None, 
                        unconditional_guidance_scale=1., unconditional_conditioning=None,
                        uc_type=None,):
        t_in = t
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
        else:
            # with unconditional condition
            if isinstance(c, torch.Tensor):
                x_in = torch.cat([x] * 2)
                t_in = torch.cat([t] * 2)
                c_in = torch.cat([unconditional_conditioning, c])
                model_out_uncond, model_out = self.apply_model(x_in, t_in, c_in, return_ids=return_codebook_ids).chunk(2)
            elif isinstance(c, dict):
                model_out = self.apply_model(x, t, c, return_ids=return_codebook_ids)
                model_out_uncond = self.apply_model(x, t, unconditional_conditioning, return_ids=return_codebook_ids)
            else:
                raise NotImplementedError
            if uc_type is None:
                model_out = model_out_uncond + unconditional_guidance_scale * (model_out - model_out_uncond)
            else:
                if uc_type == 'cfg_original':
                    model_out = model_out + unconditional_guidance_scale * (model_out - model_out_uncond)
                elif uc_type == 'cfg_ours':
                    model_out = model_out + unconditional_guidance_scale * (model_out_uncond - model_out)
                else:
                    raise NotImplementedError

        if score_corrector is not None:
            assert self.parameterization == "eps"
            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)

        if return_codebook_ids:
            model_out, logits = model_out

        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        else:
            raise NotImplementedError()

        if clip_denoised:
            x_recon.clamp_(-1., 1.)
        if quantize_denoised:
            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
        if return_codebook_ids:
            return model_mean, posterior_variance, posterior_log_variance, logits
        elif return_x0:
            return model_mean, posterior_variance, posterior_log_variance, x_recon
        else:
            return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                 unconditional_guidance_scale=1., unconditional_conditioning=None,
                 uc_type=None,):
        b, *_, device = *x.shape, x.device
        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
                                       return_codebook_ids=return_codebook_ids,
                                       quantize_denoised=quantize_denoised,
                                       return_x0=return_x0,
                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs,
                                       unconditional_guidance_scale=unconditional_guidance_scale, 
                                       unconditional_conditioning=unconditional_conditioning,
                                       uc_type=uc_type,)
        if return_codebook_ids:
            raise DeprecationWarning("Support dropped.")
        elif return_x0:
            model_mean, _, model_log_variance, x0 = outputs
        else:
            model_mean, _, model_log_variance = outputs

        noise = noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))

        if return_codebook_ids:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
        if return_x0:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
        else:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
                              log_every_t=None):
        if not log_every_t:
            log_every_t = self.log_every_t
        timesteps = self.num_timesteps
        if batch_size is not None:
            b = batch_size if batch_size is not None else shape[0]
            shape = [batch_size] + list(shape)
        else:
            b = batch_size = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=self.device)
        else:
            img = x_T
        intermediates = []
        if cond is not None:
            if isinstance(cond, dict):
                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
            else:
                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
                        total=timesteps) if verbose else reversed(
            range(0, timesteps))
        if type(temperature) == float:
            temperature = [temperature] * timesteps

        for i in iterator:
            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != 'hybrid'
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))

            img, x0_partial = self.p_sample(img, cond, ts,
                                            clip_denoised=self.clip_denoised,
                                            quantize_denoised=quantize_denoised, return_x0=True,
                                            temperature=temperature[i], noise_dropout=noise_dropout,
                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
            if mask is not None:
                assert x0 is not None
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1. - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(x0_partial)
            if callback: callback(i)
            if img_callback: img_callback(img, i)
        return img, intermediates

    @torch.no_grad()
    def p_sample_loop(self, cond, shape, return_intermediates=False,
                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
                      mask=None, x0=None, img_callback=None, start_T=None,
                      log_every_t=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None,
                      uc_type=None,):

        if not log_every_t:
            log_every_t = self.log_every_t
        device = self.betas.device
        b = shape[0]
        
        # sample an initial noise
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T
        
        intermediates = [img]
        if timesteps is None:
            timesteps = self.num_timesteps

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
            range(0, timesteps))

        if mask is not None:
            assert x0 is not None
            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match

        for i in iterator:
            ts = torch.full((b,), i, device=device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != 'hybrid'
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))

            img = self.p_sample(img, cond, ts,
                                clip_denoised=self.clip_denoised,
                                quantize_denoised=quantize_denoised,
                                unconditional_guidance_scale=unconditional_guidance_scale,
                                unconditional_conditioning=unconditional_conditioning,
                                uc_type=uc_type)
            if mask is not None:
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1. - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(img)
            if callback: callback(i)
            if img_callback: img_callback(img, i)

        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
               verbose=True, timesteps=None, quantize_denoised=False,
               mask=None, x0=None, shape=None, **kwargs):
        if shape is None:
            shape = (batch_size, self.channels, self.total_length, *self.image_size)
        if cond is not None:
            if isinstance(cond, dict):
                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
            else:
                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
        return self.p_sample_loop(cond,
                                  shape,
                                  return_intermediates=return_intermediates, x_T=x_T,
                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
                                  mask=mask, x0=x0,)

    @torch.no_grad()
    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):

        if ddim:
            ddim_sampler = DDIMSampler(self)
            shape = (self.channels, self.total_length, *self.image_size)
            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
                                                        shape,cond,verbose=False, **kwargs)

        else:
            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
                                                 return_intermediates=True, **kwargs)

        return samples, intermediates
    
    @torch.no_grad()
    def log_condition(self, log, batch, xc, x, c, cond_stage_key=None):
        """ 
        xc: oringinal condition before enconding. 
        c: condition after encoding.
        """
        if x.dim() == 5:
            txt_img_shape = [x.shape[3], x.shape[4]]
        elif x.dim() == 4:
            txt_img_shape = [x.shape[2], x.shape[3]]
        else:
            raise ValueError
        if self.model.conditioning_key is not None: #concat-time-mask
            if hasattr(self.cond_stage_model, "decode"):
                xc = self.cond_stage_model.decode(c)
                log["conditioning"] = xc
            elif cond_stage_key in ["caption", "txt"]:
                log["conditioning_txt_img"] = log_txt_as_img(txt_img_shape, batch[cond_stage_key], size=x.shape[3]//25)
                log["conditioning_txt"] = batch[cond_stage_key]
            elif cond_stage_key == 'class_label':
                try:
                    xc = log_txt_as_img(txt_img_shape, batch["human_label"], size=x.shape[3]//25)
                except:
                    xc = log_txt_as_img(txt_img_shape, batch["class_name"], size=x.shape[3]//25)
                log['conditioning'] = xc
            elif isimage(xc):
                log["conditioning"] = xc
            if ismap(xc):
                log["original_conditioning"] = self.to_rgb(xc)
            if isinstance(c, dict) and 'mask' in c:
                log['mask'] =self.mask_to_rgb(c['mask'])
        return log
    
    @torch.no_grad()
    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., unconditional_guidance_scale=1.0, 
                   first_stage_key2=None, cond_key2=None,
                   c=None, 
                   **kwargs):
        """ log images for LatentDiffusion """
        use_ddim = ddim_steps is not None
        is_imgs = first_stage_key2 is not None
        if is_imgs:
            assert(cond_key2 is not None)
        log = dict()

        # get input
        z, c, x, xrec, xc = self.get_input(batch, 
                                           k=self.first_stage_key if first_stage_key2 is None else first_stage_key2,
                                           return_first_stage_outputs=True,
                                           force_c_encode=True,
                                           return_original_cond=True,
                                           bs=N,
                                           cond_key=cond_key2 if cond_key2 is not None else None,
                                           )
        
        N_ori = N
        N = min(z.shape[0], N)
        n_row = min(x.shape[0], n_row)

        if unconditional_guidance_scale != 1.0:
            prompts = N * self.temporal_length * [""] if (is_imgs and not self.static_video) else N * [""]
            uc = self.get_condition_validate(prompts)
            
        else:
            uc = None

        log["inputs"] = x
        log["reconstruction"] = xrec
        log = self.log_condition(log, batch, xc, x, c, 
                                 cond_stage_key=self.cond_stage_key if cond_key2 is None else cond_key2
        )
        
        if sample:
            with self.ema_scope("Plotting"):
                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
                                                         ddim_steps=ddim_steps,eta=ddim_eta,
                                                         temporal_length=self.video_length,
                                                         unconditional_guidance_scale=unconditional_guidance_scale,
                                                         unconditional_conditioning=uc, **kwargs,
                                                         )
            # decode samples
            x_samples = self.decode_first_stage(samples)
            log["samples"] = x_samples
        return log

    def configure_optimizers(self):
        """ configure_optimizers for LatentDiffusion """
        lr = self.learning_rate
        
        # --------------------------------------------------------------------------------
        # set parameters
        if hasattr(self, "only_optimize_empty_parameters") and self.only_optimize_empty_parameters:
            print("[INFO] Optimize only empty parameters!")
            assert(hasattr(self, "empty_paras"))
            params = [p for n, p in self.model.named_parameters() if n in self.empty_paras]
        elif hasattr(self, "only_optimize_pretrained_parameters") and self.only_optimize_pretrained_parameters:
            print("[INFO] Optimize only pretrained parameters!")
            assert(hasattr(self, "empty_paras"))
            params = [p for n, p in self.model.named_parameters() if n not in self.empty_paras]
            assert(len(params) != 0)
        elif getattr(self, "optimize_empty_and_spatialattn", False):
            print("[INFO] Optimize empty parameters + spatial transformer!")
            assert(hasattr(self, "empty_paras"))
            empty_paras = [p for n, p in self.model.named_parameters() if n in self.empty_paras]
            SA_list = [".attn1.", ".attn2.", ".ff.", ".norm1.", ".norm2.", ".norm3."]
            SA_params = [p for n, p in self.model.named_parameters() if check_istarget(n, SA_list)]
            if getattr(self, "spatial_lr_decay", False):
                params = [
                    {"params": empty_paras},
                    {"params": SA_params, "lr": lr * self.spatial_lr_decay}
                ]
            else:
                params = empty_paras + SA_params
        else:
            # optimize whole denoiser
            if hasattr(self, "spatial_lr_decay") and self.spatial_lr_decay:
                print("[INFO] Optimize the whole net with different lr!")
                print(f"[INFO] {lr} for empty paras, {lr * self.spatial_lr_decay} for pretrained paras!")
                empty_paras = [p for n, p in self.model.named_parameters() if n in self.empty_paras]
                # assert(len(empty_paras) == len(self.empty_paras)) # self.empty_paras:cond_stage_model.embedding.weight not in diffusion model params
                pretrained_paras = [p for n, p in self.model.named_parameters() if n not in self.empty_paras]
                params = [
                    {"params": empty_paras},
                    {"params": pretrained_paras, "lr": lr * self.spatial_lr_decay}
                ]
                print(f"[INFO] Empty paras: {len(empty_paras)}, Pretrained paras: {len(pretrained_paras)}")

            else:
                params = list(self.model.parameters())
        
        if hasattr(self, "generator_trainable") and not self.generator_trainable:
            # fix unet denoiser
            params = list()

        if self.inject_unet:
            params = itertools.chain(*self.lora_require_grad_params)
                
        if self.inject_clip:
            if self.inject_unet:
                params = list(params)+list(itertools.chain(*self.lora_require_grad_params_clip))
            else:
                params = itertools.chain(*self.lora_require_grad_params_clip)
            

        # append paras
        # ------------------------------------------------------------------
        def add_cond_model(cond_model, params):
            if isinstance(params[0], dict):
                # parameter groups
                params.append({"params": list(cond_model.parameters())})
            else:
                # parameter list: [torch.nn.parameter.Parameter]
                params = params + list(cond_model.parameters())
            return params
        # ------------------------------------------------------------------
        
        if self.cond_stage_trainable:
            # print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
            params = add_cond_model(self.cond_stage_model, params)
        
        if self.learn_logvar:
            print('Diffusion model optimizing logvar')
            if isinstance(params[0], dict):
                params.append({"params": [self.logvar]})
            else:
                params.append(self.logvar)
        
        # --------------------------------------------------------------------------------
        opt = torch.optim.AdamW(params, lr=lr)
        
        # lr scheduler
        if self.use_scheduler:
            assert 'target' in self.scheduler_config
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                }]
            return [opt], scheduler
        
        return opt
    
    @torch.no_grad()
    def to_rgb(self, x):
        x = x.float()
        if not hasattr(self, "colorize"):
            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
        x = nn.functional.conv2d(x, weight=self.colorize)
        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
        return x

    @torch.no_grad()
    def mask_to_rgb(self, x):
        x = x * 255
        x = x.int()
        return x

class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)
        print('Successfully initialize the diffusion model !')
        self.conditioning_key = conditioning_key
        # assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'resblockcond', 'hybrid-adm', 'hybrid-time']

    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
                c_adm=None, s=None, mask=None, **kwargs):
        # temporal_context = fps is foNone
        if self.conditioning_key is None:
            out = self.diffusion_model(x, t, **kwargs)
        elif self.conditioning_key == 'concat':
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t, **kwargs)
        elif self.conditioning_key == 'crossattn':
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc, **kwargs)
        elif self.conditioning_key == 'hybrid':
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, **kwargs)
        elif self.conditioning_key == 'resblockcond':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, context=cc, **kwargs)
        elif self.conditioning_key == 'adm':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc, **kwargs)
        elif self.conditioning_key == 'hybrid-adm':
            assert c_adm is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
        elif self.conditioning_key == 'hybrid-time':
            assert s is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, s=s, **kwargs)
        elif self.conditioning_key == 'concat-time-mask':
            # assert s is not None
            # print('x & mask:',x.shape,c_concat[0].shape)
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t, context=None, s=s, mask=mask, **kwargs)
        elif self.conditioning_key == 'concat-adm-mask':
            # assert s is not None
            # print('x & mask:',x.shape,c_concat[0].shape)
            if c_concat is not None:
                xc = torch.cat([x] + c_concat, dim=1)
            else:
                xc = x
            out = self.diffusion_model(xc, t, context=None, y=s, mask=mask, **kwargs)
        elif self.conditioning_key == 'crossattn-adm':
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc, y=s, **kwargs)
        elif self.conditioning_key == 'hybrid-adm-mask':
            cc = torch.cat(c_crossattn, 1)
            if c_concat is not None:
                xc = torch.cat([x] + c_concat, dim=1)
            else:
                xc = x
            out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask, **kwargs)
        elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
            # assert s is not None
            assert c_adm is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm, **kwargs)
        else:
            raise NotImplementedError()

        return out


class T2VAdapterDepth(LatentDiffusion):
    def __init__(self, depth_stage_config, adapter_config, *args, **kwargs):
        super(T2VAdapterDepth, self).__init__(*args, **kwargs)
        self.adapter = instantiate_from_config(adapter_config)
        self.condtype = adapter_config.cond_name
        self.depth_stage_model = instantiate_from_config(depth_stage_config)

    def prepare_midas_input(self, batch_x):
        # input: b,c,h,w
        x_midas = torch.nn.functional.interpolate(batch_x, size=(384, 384), mode='bicubic')
        return x_midas
    
    @torch.no_grad()
    def get_batch_depth(self, batch_x, target_size, encode_bs=1):
        b, c, t, h, w = batch_x.shape
        merge_x = rearrange(batch_x, 'b c t h w -> (b t) c h w')
        split_x = torch.split(merge_x, encode_bs, dim=0)
        cond_depth_list = []
        for x in split_x:
            x_midas = self.prepare_midas_input(x)
            cond_depth = self.depth_stage_model(x_midas)
            cond_depth = torch.nn.functional.interpolate(
                    cond_depth,
                    size=target_size,
                    mode="bicubic",
                    align_corners=False,
                )
            depth_min, depth_max = torch.amin(cond_depth, dim=[1, 2, 3], keepdim=True), torch.amax(cond_depth, dim=[1, 2, 3], keepdim=True)
            cond_depth = 2. * (cond_depth - depth_min) / (depth_max - depth_min + 1e-7) - 1.
            cond_depth_list.append(cond_depth)
        batch_cond_depth=torch.cat(cond_depth_list, dim=0)
        batch_cond_depth = rearrange(batch_cond_depth, '(b t) c h w -> b c t h w', b=b, t=t)
        return batch_cond_depth
    
    def get_adapter_features(self, extra_cond, encode_bs=1):
        b, c, t, h, w = extra_cond.shape
        ## process in 2D manner
        merge_extra_cond = rearrange(extra_cond, 'b c t h w -> (b t) c h w')
        split_extra_cond = torch.split(merge_extra_cond, encode_bs, dim=0)
        features_adapter_list = []
        for extra_cond in split_extra_cond:
            features_adapter = self.adapter(extra_cond)
            features_adapter_list.append(features_adapter)
        merge_features_adapter_list = []
        for i in range(len(features_adapter_list[0])):
            merge_features_adapter = torch.cat([features_adapter_list[num][i] for num in range(len(features_adapter_list))], dim=0)
            merge_features_adapter_list.append(merge_features_adapter)
        merge_features_adapter_list = [rearrange(feature, '(b t) c h w -> b c t h w', b=b, t=t) for feature in merge_features_adapter_list]
        return merge_features_adapter_list
